Repositório Colecção:http://hdl.handle.net/10198/23122015-08-02T22:30:22Z2015-08-02T22:30:22ZLost and win-back customers: towards a theoretical framework of customer relationship reactivationLopes, LuísaBrito, Carlos HenriqueAlves, Helenahttp://hdl.handle.net/10198/101332014-08-14T01:00:50Z2012-01-01T00:00:00ZTítulo: Lost and win-back customers: towards a theoretical framework of customer relationship reactivation
Autor: Lopes, Luísa; Brito, Carlos Henrique; Alves, Helena
Resumo: Research has shown that there is a negative correlation between the number of "lost customers" and business income. Stauss and Friege (1999) have
found that the net return on investment from a new customer is 23% compared to a 214% return on investment from the reinstatement of a customer
who has defected. Customer win-back is an important part of a customer relationship management strategy and focuses on the re-initiation and
management of relationships with customers that have lapsed or defected from a firm (Thomas, Blattberg, and Fox, 2004). This study presents an
ongoing doctoral research and is mainly conceptual in nature. It develops a theoretical framework of Customer Relationship Reactivation in B2C services
and is interested in a dual analysis relating relationship dissolution and reactivation in B2C services. The research questions are:
•Why do some ended relationships reactivate?
• How does the process of reactivation develop in B2C services?2012-01-01T00:00:00ZMaintenance behaviour-based prediction system using data miningBastos, PedroLopes, Rui PedroPires, L.C.M.Pedrosa, Tiagohttp://hdl.handle.net/10198/72372014-04-17T01:08:20Z2009-01-01T00:00:00ZTítulo: Maintenance behaviour-based prediction system using data mining
Autor: Bastos, Pedro; Lopes, Rui Pedro; Pires, L.C.M.; Pedrosa, Tiago
Resumo: In the last years we have assisted to several and deep changes in
industrial manufacturing. Induced by the need of increasing
efficiency, bigger flexibility, better quality and lower costs, it
became more complex. The complexity of this new scenario has
caused big pressure under enterprises production systems and
consequently in its maintenance systems. Manufacturing systems
recognize high level costs due equipment breakdown, motivated by
the time spent to repair, which corresponds to no production time
and scrapyard, and also money spent in repair actions. Usually,
enterprises do not share data produced from their maintenance
interventions. This investigation intends to create an
organizational architecture that integrates data produced in
factories on their activities of reactive, predictive and preventive
maintenance. The main idea is to develop a decentralized predictive
maintenance system based on data mining concepts. Predicting the
possibility of breakdowns with bigger accuracy will increase
systems reliability.2009-01-01T00:00:00Z